Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (11): 1685-1697.doi: 10.23940/ijpe.20.11.p1.16851697

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A Hybrid Approach for the Evaluation of Rail Monitoring and Maintenance Strategies for the Grand Paris Express New Metro

Laurent Bouillauta,*, Olivier Françoisa, Yves Putallazb, Clément Granierb and Christophe Cieuxc   

  1. aUniversité Gustave Eiffel COSYS/GRETTIA, 77447 Marne-la-Vallée, France;
    bIMDM - Infra Consulting, 1800 Vevey, Switzerland;
    cSociété du Grand Paris, Systems and Safety unit, 93200 St Denis, France
  • Submitted on ; Revised on ; Accepted on
  • Contact: *E-mail address:

Abstract: Three years after its 2010 enactment, the French government proposed a timeline for the development of a new metro network providing new rapid transit lines in the Ile de France region. Implemented by the Société du Grand Paris (SGP), the Grand Paris Express (GPE) thus became the largest transport project in Europe. As in any new railway project, a safety record must be established by the SGP. Among all criteria that had to be investigated, the ability of the network manager to prevent and detect broken rails was a particularly sensitive point. Indeed, beyond the obvious consequences induced by a broken rail impacting the safety of passengers, such an event has a very strong impact on the availability of the infrastructure, which is a key point for all metro line automation projects. The SGP therefore required some decision support tools to enable us to evaluate the consequences of a broken rail on the network operating conditions and, moreover, to determine the best monitoring strategy and the adequacy of the maintenance policy to prevent broken rails. Based on a former study commissioned by RATP (the historic operator and infrastructure manager of the Paris metro network) dealing with the evaluation and optimization of detection and prevention of broken rails in a metro line automation context, the SGP wanted to possess an equivalent decision support tool customised to the needs and the characteristics of the GPE. Nevertheless, in the GPE context, the network that was considered does not yet exist. Moreover, an equivalent system does not exist. Therefore, no feedback databases are available to estimate reliability parameters necessary for such decision support tools. Therefore, the present paper proposes an original approach combining physical modelling of rail deterioration with statistics to overcome this limitation.

Key words: reliability analysis, maintenance optimization, broken rails, stochastic modelling, mechanical modelling of rail degradation, Bayesian networks, hybrid learning approach